We compare our results with SRC [1]}, LC-KSVD [2]}, DeepSC [3]}, DSN [4]} and other state-of-the-art approaches: ScSPM [5]}, LLC [6]}, ITDL [7]}, ISPR+IFV [8]}, SR-LSR [9]}, DeCAF [10]}, DSFL+DeCAF [11]}. The detailed comparison results are shown in Table 5. Compared to LC-KSVD, S-DSN(relu)-1's performance is much better, since it makes a \(5.9\%\)  improvement. It also registers about \(1.8\%\)  improvement over the deep models: DeepSC, DeCAF, DSFL+DeCAF and DSN. As Table 5 shows, we see that S-DSN(relu) performs best among all existing methods. The confusion matrix for the S-DSN(relu) are further shown in Figure 2, from which we can see that the misclassification errors of industrial and store are higher than others.
